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Web Log Analysis in Genealogy System

Web Log Analysis in Genealogy System

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Xiaojian Liu Yi Zhu Shengwei Ji Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

A large amount of log data is generated every moment in many real-world applications. System logs, mobile application logs, web logs etc., all of which contain rich information and can be analyzed for valuable information. At the same time, genealogy attracts more and more attention in recent decades, especially for Chinese. People utilize the genealogy system to seek roots, find source of family, build a family tree and so on. Due to the complexity of genealogy data and specific features of genealogy system, how to extract and analyze the valued genealogy information effectively is a huge challenge. Motivated by this, we propose a novel analysis method using web logs in genealogy system for drawing user profile and providing personalized recommendations. More specifically, firstly, all web logs are extracted from genealogy system based on the enhanced logs which record the behaviors and data of user. Secondly, the extracted logs are formatted for analysis, and we analyze the logs to extract various user-related attributes for drawing user profile. Finally, personalized recommendations like interested people and family tree are provided for each user in system. The high availability and effectiveness of our proposed method is verified on genealogy system-Huapu system.

关键词： data mining databases data visualization Tools Big data Feature extraction knowledge engineering

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Selecting Good Expansion Terms for Improving XML Retrieval Performance

Selecting Good Expansion Terms for Improving XML Retrieval P...

引用

International Conference on Control engineering and Communication Technology (ICCECT)

作者： Minjuan Zhong Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

In this paper, we study how to perform XML query expansion effectively from the high quality pseudo-relevance documents. A solution for selecting good expansion information is presented, in which various features impacting weight, such as term element frequency, term inverse element frequency, semantic weight of tag and level information, are analyzed and those term with high weigh value are selected as expansion term. Experiment results show that proposed expansion method is feasible. Compared to original query and traditional expansion method with no structure features considered, our method achieves better retrieval performance.

关键词： XML Semantics Analytical models Vectors Information retrieval Computational modeling Educational institutions

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Rule-enhanced Noisy knowledge Graph Embedding via Low-quality Error Detection

Rule-enhanced Noisy Knowledge Graph Embedding via Low-qualit...

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Yan Hong Chenyang Bu Tingting Jiang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

knowledge graphs (KGs) have been widely applied in many fields such as recommendation systems and knowledge reasoning. Embedding KGs into a continuous vector space has quickly gained significant attention. However, most traditional KG embedding models assume that all the facts in the existing KGs are completely correct, ignoring that KG construction usually involves automatic mechanisms. These automatic construction processes inevitably generate a lot of noises and conflicts, including low-quality errors (e.g., entity type errors). Moreover, these low-quality noises could greatly influence the quality of rule extraction, which may reduce the efficiency of Rule-Guided Embedding model (RUGE). To address this problem, an efficient method to eliminate those entity type errors in triples is proposed and applied to RUGE. Experimental results demonstrate that the filtering of low-quality noises can greatly improve the accuracy of knowledge representation learning as well as the quality of rules, further illustrating the effectiveness of our method.

关键词： Noise measurement Semantics knowledge representation data mining Predictive models data models

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An Abnormal data Analysis and Processing Method for Genealogy Graph databases

An Abnormal Data Analysis and Processing Method for Genealog...

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Jianxuan Shao Guliu Liu Shengwei Ji Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Large amounts of data are generated continually in the real world. The objects in the data and the relationships between them have become increasingly complex. Graph is a powerful tool for representing these data and the complex relationships between them. To effectively describe the entities and connections in these data, the concept of a knowledge graph was proposed, and knowledge graph has become one of the bases for storing graph data, and the importance of it is self-evident. Genealogy data is a kind of graph data where nodes are used to represent the person in the genealogy data, and edges are used to represent the relationship between the person. Moreover, in a genealogy graph database, the "critical" relationship that is unique for an entity is defined. In contrast, the abnormal data derive from the existence of multiple "critical" relationships between entities. In a genealogy graph, abnormal data will cause the wrong relationships and redundant entities. To avoid such abnormal data in genealogy graph database, the abnormal data are categorized into four different types, and the corresponding processing methods are proposed for each type of abnormal data, respectively. The experiment results demonstrate that the processing method can effectively solve these abnormal data.

关键词： databases Biology Task analysis Anomaly detection data models knowledge engineering Big data

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Evolutionary Algorithms with Heuristic Gradient-based Repair for Constrained Optimization

Evolutionary Algorithms with Heuristic Gradient-based Repair...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Du, Jiacheng Bu, Chenyang Liu, Yuxin Liu, Fei Luo, Wenjian Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Anhui Hefei230027 China Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518000 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Gradient-based repair aims to repair infeasible solutions to feasible ones using the gradient information of the constraints. As an effective constraint handling method, gradientbased repair has received extensive attention and has been applied in various evolutionary algorithms (EAs). Nevertheless, due to the complexity of constraints in practical problems, a single infeasible solution often needs to be repaired multiple times until it becomes a feasible solution or reaches the maximum number of repairs. As far as we know, existing related research on gradient-based repair mainly applies this method directly to EAs, while there is little work in the evolutionary computing community on how to improve gradient-based repair. Currently, the multiple repairs for a single individual are independent. That is, the current repair does not consider the previous repair experience. However, only using gradient information to repair infeasible individuals may result in oscillations in the search process. Therefore, in this paper, we propose a heuristic gradient-based repair method (HGR) which exploits the previous repair information of an individual to alleviate this issue. Experimental results on several benchmarks demonstrate the effectiveness of the proposed method. The source code is available at https://***/DMiC-Lab-HFUT/HGR-SMC2022. © 2022 IEEE.

关键词： Constrained optimization

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Unsupervised vehicle re-identification with progressive adaptation 29

Unsupervised vehicle re-identification with progressive adap...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Peng, Jinjia Wang, Yang Wang, Huibing Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University Liaoning Dalian China Pengcheng Laboratory Shenzhen Guangdong China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China

ISBN: (纸本)9780999241165

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes;worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as "pseudo target samples". These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Vehicles

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Selection of multimedia data source based on user feedback

Selection of multimedia data source based on user feedback

引用

2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Deng, Song Wan, Changxuan Liu, Xiping School of Information Technology Jiangxi University of Finance and Ecomomics Nanchang China School of Software Jiangxi Agricultural University Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9780769545448

There are hundreds or thousands of web data sources providing data of relevance to a particular domain on the Web, so how to find a suitable set of sources quickly to integrate from a number of sources is becoming more and more important. Traditional data sources selection methods that are based on data quality do not consider quality characteristics of data sources under the different domains, but select the same quality dimensions for quality evaluation models. As a result, accuracies of a traditional method under different domains are quite different. In light of this, we propose a Web multimedia source selection method based on user feedback. We gain accurate core quality dimensions of data sources under the multimedia domain based on user feedback, and then achieve a quality estimation model of the multimedia data sources. The experiments on hundreds of real-world multimedia data sources have proved that our approach is efficient and accurate. © 2011 IEEE.

关键词： Accuracy Computers data Source data models databases Equations Mathematical model Multimedia Multimedia communication Quality Dimensions User Feedback Web data source data quality estimation model quality evaluation model quality multimedia computing mult

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Computing Degree of Association Based on Different Semantic Relationships

Computing Degree of Association Based on Different Semantic ...

引用

International Workshop on database and Expert Systems Applications

作者： Xuan Tian Xiaoyong Du Haihua Li School of Information Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

In domain ontologies, there is usually no weight assigned to the link between two concepts. This has been considered as one of main obstacles in using ontologies. Semantic Association (SA) is to depict the correlation of two concepts, and can be measured as the weight of the link. In this paper, we defined Degree of Association (DOA) to measure SA from a concept to its direct-related concept in domain ontology, and proposed a Language-Model-Based Method (LMBM) to compute DOA. Our idea comes from the intuition that the semantic relationship between two concepts implies certain semantic association of them. We took probabilistic model for computing DOA, and used Maximum Likelihood Estimation to estimate parameters. We tested the proposed method on two different domain ontologies, and applied it in experiments of semantic query expansion. Experimental results show the benefit of our approach and demonstrate the promising effectiveness over semantic query expansion.

关键词： Ontologies Information retrieval Direction of arrival estimation Parameter estimation data mining databases Expert systems Computer applications Laboratories data engineering

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An Economic Semantic Web Platform

An Economic Semantic Web Platform

引用

International Conference on Grid and Cooperative Computing Workshops (GCCW)

作者： Xiaoyong Du He Hu Man Li Wenjuan Wu Guo Qin Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing China

The paper describes an ongoing project which implements a subject-oriented semantic Web platform at Renmin Univ. of China. The economic semantic Web platform (ESWP) contains three components: collaborative ontology developing environment and repository system (CODERS); economic ontology annotation Web services (ConAnnotator); economic ontology and annotated resources. We describe each of these components in detail and illustrate some use cases of the ESWP

关键词： Semantic Web Ontologies Environmental economics Web services Collaboration Humans Software agents Application software Helium Laboratories

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In-Network Execution of External Join for Sensor Networks

In-Network Execution of External Join for Sensor Networks

引用

International Conference on Web-Age Information Management Workshops (WAIM)

作者： Yongxuan Lai Yilong Chen Hong Chen School of Information Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

ISBN: (纸本)9781424432004;9780769531854

Recently there have been growing interests in the applications of wireless sensor networks such as traffic tracking, environmental surveillance, and network monitoring. In these applications, the exploration of the relationship and linkage of sensing data with other data sources can be naturally expressed by the external join, where the sensory tuples join with an external table at the base station. However, executing such kind of join queries in a highly distributed and resource-constraint sensor network is a challenging task. In this paper, we propose a partition-based algorithm called NEJA (in-network external join algorithm) for the external join processing in sensor networks. NEJA organizes the sensory data of the network through an optimized "value-to-storage" mapping, according to which each storage point stores the tuples that belong to the same subrange on the joint attribute. Then the subrange of each storage point is further partitioned into unit ranges, and tuples in the same unit range wisely choose their joining point that incurs the least communication cost based on a cost metric according to the latest historical statistics. Also, NEJA adopts some optimization techniques to handle the changes of sensory data and uses approximate approaches to cut down the maintenance cost of the mechanism. The experimental results indicate that our scheme is effective in reducing the amount of transmissions for the real time external join processing, especially when the external table has a relatively large size.

关键词： Base stations Joints Wireless sensor networks Sensors Partitioning algorithms Distributed databases Energy efficiency

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